The personal blog of a Toronto based software mechanic, musician, sound designer, and theatre enthusiast.

I’m writing this in lieu of a traditional Firefox Front-end Performance Update, as I think this will be more useful in the long run than just a snapshot of what my team is doing.

I want to talk about main thread disk access (sometimes referred to more generally as “main thread IO”). Specifically, I’m going to argue that main thread disk access is lethal to program responsiveness. For some folks reading this, that might be an obvious argument not worth making, or one already made ad nauseam — if that’s you, this blog post is probably not for you. You can go ahead and skip most or all of it, if you’d like. Or just skim it. You never know — there might be something in here you didn’t know or hadn’t thought about!

Disclaimer: I wouldn’t call myself a disk specialist. I don’t work for Western Digital or Seagate. I don’t design file systems. I have, however, been using and writing software for computers for a significant chunk of my life, and I seem to have accumulated a bunch of information about disks. Some of that information might be incorrect or imprecise. Please send me mail at mike dot d dot conley at gmail dot com if any of this strikes you as wildly inaccurate (though forgive me if I politely disregard pedantry), and then I can update the post.

The mechanical parts of a computer

If you grab a screwdriver and (carefully) open up a laptop or desktop computer, what do you see? Circuit boards, chips, wires and plugs. Lots of electrons flowing around in there, moving quickly and invisibly.

Notably, there aren’t many mechanical moving parts of a modern computer. Nothing to grease up, nowhere to pour lubricant. Opening up my desktop at home, the only moving parts I can really see are the cooling fans near the CPU and power supply (and if you’re like me, you’ll also notice that your cooling fans are caked with dust and in need of a cleaning).

There’s another moving part that’s harder to see — the hard drive. This might not be obvious, because most mechanical drives (I’ve heard them sometimes referred to as magnetic drives, spinny drives, physical drives, platter drives and HDDs. There are probably more terms.) hide their moving parts inside of the disk enclosure.1

If you ever get the opportunity to open one of these enclosures (perhaps the disk has failed or is otherwise being replaced, and you’re just about to get rid of it) I encourage you to.

As you disassemble the drive, what you’ll probably notice are circular parts, layered on top of one another on a motor that spins them. In between those circles are little arms that can move back and forth. This next image shows one of those circles, and one of those little arms.

There are several of those circles stacked on top of one another, and several of those arms in between them. We’re only seeing the top one in this photo.

Does this remind you of anything? The circular parts remind me of CDs and DVDs, but the arms reaching across them remind me of vinyl players.

Vinyl’s back, baby!

The comparison isn’t that outlandish. If you ignore some of the lower-level details, CDs, DVDs, vinyl players and hard drives all operate under the same basic principles:

The circular part has information encoded on it.

An arm of some kind is able to reach across the radius of the circular part.

Because the circular part is spinning, the arm is able to reach all parts of it.

The end of the arm is used to read the information encoded on it.

There’s some extra complexity for hard drives. Normally there’s more than one spinning platter and one arm, all stacked up, so it’s more like several vinyl players piled on top of one another.

Hard drives are also typically written to as well as read from, whereas CDs, DVDs and vinyls tend to be written to once, and then used as “read-only memory.” (Though, yes, there are exceptions there.)

Lastly, for hard drives, there’s a bit I’m skipping over involving caches, where parts of the information encoded on the spinning platters are temporarily held elsewhere for easier and faster access, but we’ll ignore that for now for simplicity, and because it wrecks my simile.2

So, in general, when you’re asking a computer to read a file off of your hard drive, it’s a bit like asking it to play a tune on a vinyl. It needs to find the right starting place to put the needle, then it needs to put the needle there and only then will the song play.

For hard drives, the act of moving the “arm” to find the right spot is called seeking.

Contiguous blocks of information and fragmentation

Have you ever had to defragment your hard drive? What does that even mean? I’m going to spend a few moments trying to explain that at a high-level. Again, if this is something you already understand, go ahead and skip this part.

Most functional hard drives allow you to do the following useful operations:

Just like there are different ways of organizing a sock drawer (at random, by colour, by type, by age, by amount of damage), there are ways of organizing a hard drive. These “ways” are called file systems. There are lots of different file systems. If you’re using a modern version of Windows, you’re probably using a file system called NTFS. One of the things that a file system is responsible for is knowing where your files are on the spinning platters. This file system is also responsible for knowing where there’s free space on the spinning platters to write new data to.

When you delete a file, what tends to happen is that your file system marks those sectors of the platter as places where new information can be written to, but doesn’t immediately overwrite those sectors. That’s one reason why sometimes deleted files can be recovered.

Depending on your file system, there’s a natural consequence as you delete and write files of different sizes to the hard drive: fragmentation. This kinda sounds like the actual physical disk is falling apart, but that’s not what it means. Data fragmentation is probably a more precise way of thinking about it.

Imagine you have a sheet of white paper broken up into a grid of 5 boxes by 5 boxes (25 boxes in total), and a box of paints and paintbrushes.

Each square on the paper is white to start. Now, starting from the top-left, and going from left-to-right, top-to-bottom, use your paint to fill in 10 of those boxes with the colour red. Now use your paint to fill in the next 5 boxes with blue. Now do 3 more boxes with yellow.

So we’ve got our colour-filled boxes in neat, organized rows (red, then blue, then yellow), and we’ve got 18 of them filled, and 7 of them still white.

Now let’s say we don’t care about the colour blue. We’re okay to paint over those now with a new colour. We also want to fill in 10 boxes with the colour purple. Hm… there aren’t enough free white boxes to put in that many purple ones, but we have these 5 blue ones we can paint over. Let’s paint over them with purple, and then put the next 5 at the end in the white boxes.

So now 23 of the boxes are filled, we’ve got 2 left at the end that are white, but also, notice that the purple boxes aren’t all together — they’ve been broken apart into two sections. They’ve been fragmented.

This is an incredibly simplified model, but (I hope) it demonstrates what happens when you delete and write files to a hard drive. Gaps open up that can be written to, and bits and pieces of files end up being distributed across the platters as fragments.

This also occurs as files grow. If, for example, we decided to paint two more white boxes red, we’d need to paint the ones at the very end, breaking up the red boxes so that they’re fragmented.

So going back to our vinyl player example for a second — the ideal scenario is that you start a song at the beginning and it plays straight through until the end, right? The more common case with disk drives, however, is you read bits and pieces of a song from different parts of the vinyl: you have to lift and move the arm each time until eventually you have heard the song from start to finish. That seeking of the arm adds overhead to the time it takes to listen to the song from beginning to end.

When your hard drive undergoes defragmentation, what your computer does is try to re-organize your disk so that files are in contiguous sectors on the platters. That’s a fancy way of saying that they’re all in a row on the platter, so they can be read in without the overhead of seeking around to assemble it as fragments.

Skipping that overhead can have huge benefits to your computer’s performance, because the disk is usually the slowest part of your computer.

On the relative input / output speeds of modern computing components

I mentioned in the disclaimer at the start of this post that I’m not a disk specialist or expert. Scott Davis is probably a better bet as one of those. His bio lists an impressive wealth of experience, and mentions that he’s “a recognized expert in virtualization, clustering, operating systems, cloud computing, file systems, storage, end user computing and cloud native applications.”

I don’t know Scott at all (if you’re reading this, Hi, Scott!), but let’s just agree for now that he probably knows more about disks than I do.

I’m picking Scott as an expert because of a particularly illustrative analogy that was posted to a blog for a company he used to work for. The analogy compares the speeds of different media that can be used to store information on a computer. Specifically, it compares the following:

RAM

The network with a decent connection

Flash drives

Magnetic hard drives — what we’ve been discussing up until now.

For these media, the post claims that input / output speed can be measured using the following units:

RAM is in nanoseconds

10GbE Network speed is in microseconds (~50 microseconds)

Flash speed is in microseconds (between 20-500+ microseconds)

Disk speed is in milliseconds

That all seems pretty fast. What’s the big deal? Well, it helps if we zoom in a little bit. The post does this by supposing that we pretend that RAM speed happens in minutes.

If that’s the case, then we’d have to measure network speed in weeks.

And if that’s the case, then we’d want to measure the speed of a Flash drive in months.

And if that’s the case, then we’d have to measure the speed of a magnetic spinny disk in decades.

Update (May 23, 2019): My Uncle Mark, who also works in computing, sent me links that show similar visualizations of computing latency: this one has a really excellent infographic, and this one has more discussion. These articles highlight network latency as the worst offender, which is true especially when the quality of service is low, but I’m mostly writing this post for folks who hack on Firefox where the vast majority of networking occurs off of the main thread.

I wish I had some ACM paper, or something written by a computer science professor that I could point to you to bolster the following claim. I don’t, not because one doesn’t exist, but because I’m too lazy to look for one. I hope you’ll forgive me for that, but I don’t think I’m saying anything super controversial when I say:

In the common case, for a personal computer, it’s best to assume that reading and writing to the disk is the slowest operation you can perform.

Sure, there are edge cases where other things in the system might be slower. And there is that disk cache that I breezed over earlier that might make reading or writing cheaper. And sometimes the operating system tries to do smart things to help you. For now, just let it go. I’m making a broad generalization that I think covers the common cases, and I’m talking about what’s best to assume.

Single and multi-threaded restaurants

When I try to describe threading and concurrency to someone, I inevitably fall back to the metaphor of cooks in a kitchen in a restaurant. This is a special restaurant where there’s only one seat, for a single customer — you, the user.

Single-threaded programs

Let’s imagine a restaurant that’s very, very small and simple. In this restaurant, the cook is also acting as the waiter / waitress / server. That means when you place your order, the server / cook goes into the kitchen and makes it for you. While they’re gone, you can’t really ask for anything else — the server / cook is busy making the thing you asked for last.

This is how most simple, single-threaded programs work—the user feeds in requests, maybe by clicking a button, or typing something in, maybe something else entirely—and then the program goes off and does it and returns some kind of result. Maybe at that point, the program just exits (“The restaurant is closed! Come back tomorrow!”), or maybe you can ask for something else. It’s really up to how the restaurant / program is designed that dictates this.

Suppose you’re very, very hungry, and you’ve just ordered a complex five-course meal for yourself at this restaurant. Blanching, your server / cook goes off to the kitchen. While they’re gone, nobody is refilling your water glass or giving you breadsticks. You’re pretty sure there’s activity going in the kitchen and that the server / cook hasn’t had a heart attack back there, but you’re going to be waiting a looooong time since there’s only one person working in this place.

Maybe in some restaurants, the server / cook will dash out periodically to refill your water glass, give you some breadsticks, and update you on how things are going, but it sure would be nice if we gave this person some help back there, wouldn’t it?

Multi-threaded programs

Let’s imagine a slightly different restaurant. There are more cooks in the kitchen. The server is available to take your order (but is also able to cook in the kitchen if need be), and you make your request from the menu.

Now suppose again that you order a five-course meal. The server goes to the kitchen and tells the cooks what you just ordered. In this restaurant, suppose the kitchen staff are a really great team and don’t get in each other’s way3, so they divide up the order in a way that makes sense and get to work.

The server can come back and refill your water glass, feed you breadsticks, perhaps they can tell you an entertaining joke, perhaps they can take additional orders that won’t take as long. At any rate, in this restaurant, the interaction between the user and the server is frequent and rarely interrupted.

The waiter / waitress / server is the main thread

In these two examples, the waiter / waitress / server is what is usually called the main thread of execution, which is the part of the program that the user interacts with most directly. By moving expensive operations off of the main thread, the responsiveness of the program increases.

Have you ever seen the mouse turn into an hourglass, seen the “This program is not responding” message on Windows? Or the spinny colourful pinwheel on macOS? In those cases, the main thread is off doing something and never came back to give you your order or refill your water or breadsticks — that’s how it generally manifests in common operating systems. The program seems “unresponsive”, “sluggish”, “frozen”. It’s “hanging”, or “stuck”. When I hear those words, my immediate assumption is that the main thread is busy doing something — either it’s taking a long time (it’s making you your massive five course meal, maybe not as efficiently as it could), or it’s stuck (maybe they fell down a well!).

In either case, the general rule of thumb to improving program responsiveness is to keep the server filling the user’s water and breadsticks by offloading complex things on the menu to other cooks in the kitchen.

Accessing the disk on the main thread

Recall that in the common case, for a personal computer, it’s best to assume that reading and writing to the disk is the slowest operation you can perform. In our restaurant example, reading or writing to the disk on the main thread is a bit like having your server hop onto their bike and ride out to the next town over to grab some groceries to help make what you ordered.

And sometimes, because of data fragmentation (not everything is all in one place), the server has to search amongst many many shelves all widely spaced apart to get everything.

And sometimes the grocery store is very busy because there are other restaurants out there that are grabbing supplies.

And sometimes there are police checks (anti-virus / anti-malware software) occurring for passengers along the road, where they all have to show their IDs before being allowed through.

It’s an incredibly slow operation. Hopefully by the time the server comes back, they don’t realize they have to go back out again to get more, but they might if they didn’t realize they were missing some more ingredients.4

Slow slow slow. And unresponsive. And a great way to lose a hungry customer.

For super small programs, where the kitchen is well stocked, or the ride to the grocery store doesn’t need to happen often, having a single-thread and having it read or write is usually okay. I’ve certainly written my fair share of utility programs or scripts that do main thread disk access.

Firefox, the program I spend most of my time working on as my job, is not a small program. It’s a very, very, very large program. Using our restaurant model, it’s many large restaurants with many many cooks on staff. The restaurants communicate with each other and ship food and supplies back and forth using messenger bikes, to provide to you, the customer, the best meals possible.

But even with this large set of restaurants, there’s still only a single waiter / waitress / server / main thread of execution as the point of contact with the user.

Part of my job is to help organize the workflows of this restaurant so that they provide those meals as quickly as possible. Sending the server to the grocery store (main thread disk access) is part of the workflow that we absolutely need to strike from the list.

Start-up main-thread disk access

Going back to our analogy, imagine starting the program like opening the restaurant. The lights go on, the chairs come off of the tables, the kitchen gets warmed up, and prep begins.

While this is occurring, it’s all hands on deck — the server might be off in the kitchen helping to do prep, off getting cutlery organized, whatever it takes to get the restaurant open and ready to serve. Before the restaurant is open, there’s no point in having the server be idle, because the customer hasn’t been able to come in yet.

So if critical groceries and supplies needed to open the restaurant need to be gotten before the restaurant is open, it’s fine to send the server to the store. Somebody has to do it.

For Firefox, there are various things that need to take place before we can display any UI. At that point, it’s usually fine to do main-thread disk access, so long as all of the things being read or written are kept to an absolute minimum. Find how much you need to do, and reduce it as much as possible.

But as soon as UI is presented to the user, the restaurant is open. At that point, the server should stay off their bike and keep chatting with the customer, even if the kitchen hasn’t finished setting up and getting all of their supplies. So to stay responsive, don’t do disk access on the main thread of execution after you’ve started to show the user some kind of UI.

Disk contention

There’s one last complication I want to capture here with our restaurant example before I wrap up. I’ve been saying that it’s important to send anyone except the server to the grocery store for supplies. That’s true — but be careful of sending too many other people at the same time.

Moving disk access off of the main thread is good for responsiveness, full stop. However, it might do nothing to actually improve the overall time that it takes to complete some amount of work. Put it another way: just because the server is refilling your glass and giving you breadsticks doesn’t mean that your five-course meal is going to show up any faster.

Also, disk operations on magnetic drives do not have a constant speed. Having the disk do many things at once within a single program or across multiple programs can slow the whole set of operations down due to the overhead of seeking and context switching, since the operating system will try to serve all disk requests at once, more or less.5

Disk contention and main thread disk access is something I think a lot about these days while my team and I work on improving Firefox start-up performance.

Some questions to ask yourself when touching disk

So it’s important to be thoughtful about disk access. Are you working on code that touches disk? Here are some things to think about:

Is UI visible, and responsiveness a goal?

If so, best to move the disk access off of the main-thread. That was the main thing I wanted to capture, and I hope I’ve convinced you of that point by now.

Does the access need to occur?

As programs age and grow and contributors come and go, sometimes it’s important to take a step back and ask, “Are the assumptions of this disk access still valid? Does this access need to happen at all?” The fastest code is the code that doesn’t run at all.

What else is happening during this disk access? Can disk access be prioritized more efficiently?

This is often trickier to answer as a program continues to run. Thankfully, tools like profilers can help capture recordings of things like disk access to gain evidence of simultaneous disk access.

Start-up is a special case though, since there’s usually a somewhat deterministic / reliably stable set of operations that occur in the same way in roughly the same order during start-up. For start-up, using a tool like a profiler, you can gain a picture of the sorts of things that tend to happen during that special window of time. If you notice a lot of disk activity occurring simultaneously across multiple threads, perhaps ponder if there’s a better way of ordering those operations so that the most important ones complete first.

Can we reduce how much we need to read or write?

There are lots of wonderful compression algorithms out there with a variety of performance characteristics that might be worth pondering. It might be worth considering compressing the data that you’re storing before writing it so that the disk has to write less and read less.

Of course, there’s compression and decompression overhead to consider here. Is it worth the CPU time to save the disk time? Is there some other CPU intensive task that is more critical that’s occurring?

Can we organize the things that we want to read ahead of time so that they’re more likely to be read contiguously (without seeking the disk)?

If you know ahead of time the sorts of things that you’re going to be reading off of the disk, it’s generally a good strategy to store them in that read order. That way, in the best case scenario (the disk is defragmented), the read head can fly along the sectors and read everything in, in exactly the right order you want them. If the user has defragmented their disk, but the things you’re asking for are all out of order on the disk, you’re adding overhead to seek around to get what you want.

Supposing that the data on the disk is fragmented, I suspect having the files in order anyways is probably better than not, but I don’t think I know enough to prove it.

Flawed but useful

One of my mentors, Greg Wilson, likes to say that “all models are flawed, but some are useful”. I don’t think he coined it, but he uses it in the right places at the right times, and to me, that’s what counts.

The information in this post is not exhaustive — I glossed over and left out a lot. It’s flawed. Still, I hope it can be useful to you.

Thanks

Thanks to the following folks who read drafts of this and gave feedback:

Mandy Cheang

Emily Derr

Gijs Kruitbosch

Doug Thayer

Florian Quèze

There are also newer forms of disks called Flash disks and SSDs. I’m not really going to cover those in this post. ↩

The other thing to keep in mind is that the disk cache can have its contents evicted at any time for reasons that are out of your control. If you time it right, you can maybe increase the probability of a file you want to read being in the cache, but don’t bet the farm on it. ↩

Keen readers might notice I’m leaving out a discussion on Paging. That’s because this blog post is getting quite long, and because it kinda breaks the analogy a bit — who sends groceries back to a grocery store? ↩

I’ve never worked on an operating system, but I believe most modern operating systems try to do a bunch of smart things here to schedule disk requests in efficient ways. ↩

Hello, folks. I wanted to give a quick update on what the Firefox Front-end Performance team is up to, so let’s get into it.

The name of the game continues to be start-up performance. We made some really solid in-roads last quarter, and this year we want to continue to apply pressure. Specifically, we want to focus on reducing IO (specifically, main-thread IO) during browser start-up.

Reducing main thread IO during start-up

There are lots of ways to reduce IO – in the best case, we can avoid start-up IO altogether by not doing something (or deferring it until much later). In other cases, when the browser might be servicing events on the main thread, we can move IO onto another thread. We can also re-organize, pack or compress files differently so that they’re read off of the disk more efficiently.

If you want to change something, the first step is measuring it. Thankfully, my colleague Florian has written a rather brilliant test that lets us take accounting of how much IO is going on during start-up. The test is deterministic enough that he’s been able to write a whitelist for the various ways we touch the disk on the main thread during start-up, and that whitelist means we’ve made it much more difficult for new IO to be introduced on that thread.

That whitelist has been processed by the team, and have been turned into bugs, bucketed by the start-up phasewheretheIO is occurring. The next step is to estimate the effort and potential payoff of fixing those bugs, and then try to whittle down the whitelist.

And that’s effectively where we’re at. We’re at the point now where we’ve got a big list of work in front of us, and we have the fun task of burning that list down!

Being better at loading DLLs on Windows

While investigating the warm-up service for Windows, Doug Thayer noticed that we were loading DLLs during start-up oddly. Specifically, using a tool called RAMMap, he noticed that we were loading DLLs using “read ahead” (eagerly reading the entirety of the DLL into memory) into a region of memory marked as not-executable. This means that anytime we actually wanted to call a library function within that DLL, we needed to load it again into an executable region of memory.

Doug also noticed that we were unnecessarily doing ReadAhead for the same libraries in the content process. This wasn’t necessary, because by the time the content process wanted to load these libraries, the parent process would have already done it and it’d still be “warm” in the system file cache.

We’re not sure why we were doing this ReadAhead-into-unexecutable-memory work – it’s existence in the Firefox source code goes back many many years, and the information we’ve been able to gather about the change is pretty scant at best, even with version control. Our top hypothesis is that this was a performance optimization that made more sense in the Windows XP era, but has since stopped making sense as Windows has evolved.

UPDATE: Ehsan pointed us to this bug where the change likely first landed. It’s a long and wind-y bug, but it seems as if this was indeed a performance optimization, and efforts were put in to side-step effects from Prefetch. I suspect that later changes to how Prefetch and SuperFetch work ultimately negated this optimization.

Doug hacked together a quick prototype to try loading DLLs in a more sensible way, and the he was able to capture quite an improvement in start-up time on our reference hardware:

This graph measures various start-up metrics. The scatter of datapoints on the left show the “control” build, and they tighten up on the right with the “test” build. Lower is better.

At this point, we all got pretty excited. The next step was to confirm Doug’s findings, so I took his control and test builds, and tested them independently on the reference hardware using frame recording. There was a smaller1, but still detectable improvement in the test build. At this point, we decided it was worth pursuing.

We’re also seeing the impact reflected in Telemetry. The first Nightly build with Doug Thayer’s patch went out on April 14th, and we’re starting to see a nice dip in some of our graphs here:

This graph measures the time at which the browser window reports that it has first painted. April 14th is the second last date on the X axis, and the Y axis is time. The top-most line is plotting the 95th percentile, and there’s a nice dip appearing around April 14th.

With Firefox 67 only a few short weeks away, I thought it might be interesting to take a step back and talk about some of the work that the Firefox Front-end Performance team is shipping to users in that particular release.

To be clear, this is not an exhaustive list of the great performance work that’s gone into Firefox 67 – but I picked a few things that the front-end team has been focused on to talk about.

Stop loading things we don’t need right away

The fastest code is the code that doesn’t run at all. Sometimes, as the browser evolves, we realize that there are components that don’t need to be loaded right away during start-up, and can instead of deferred until sometime after start-up. Sometimes, that means we can wait until the very last moment to initialize some component – that’s called loading something lazily.

Here’s a list of things that either got deferred until sometime after start-up, or made lazy:

The hidden window

The Hidden Window is a mysterious chunk of code that manages the state of the global menu bar on macOS when there are no open windows. The Hidden Window is also sometimes used as a singleton DOM window where various operations can take place. On Linux and Windows, it turns out we were creating this Hidden Window far early than needs be, and now it’s quite a bit lazier.

Page style

Page Style is a menu you can find under View in the main menu bar, and it’s used to switch between alternative style sheets on a page. It’s a pretty rarely used feature from what we can tell, but we were scanning pages for their alternative stylesheets far earlier than we needed to. We were also scanning pages that we know don’t have alternative stylesheets, like the about:home / about:newtab page. Now we only scan normal web pages, and we do so only after we service the idle event queue.

Cache invalidation

The Startup Cache is an important part of Firefox startup performance. It’s primary job is to cache computations that occur during each startup so that they only have to happen every once in a while. For example, the mark-up of the browser UI often doesn’t change from startup to startup, so we can cache a version of the mark-up that’s faster to read from disk, and only invalidate that during upgrades.

Don’t touch the disk

The disk is almost always the slowest part of the system. Reading and writing to the disk can take a long time, especially on spinning magnetic drives. The less we can read and write, the better. And if we’re going to read, best to do it off of the main thread so that the UI remains responsive.

Old XUL icons code

We were reading from the disk on the main thread to search for window-specific icons to display in the window titlebar.

An impressive set of patches were recently queued to land, which should bring document splitting to WebRender, but in a disabled state. The gfx.webrender.split-render-roots pref is what controls it, but I don’t think we can reap the full benefits of document splitting until we get retained display lists enabled in the parent process for the UI. I believe, at that point, we can start enabling document splitting, which means that updating the browser UI area will not involve sending updates to the content area for WebRender.

In other WebRender news, it looks like it should be enabled by default for some of our users on the release channel in Firefox 67, due to be released in mid-May!

Warm-up Service (In-Progress by Doug Thayer)

Doug has written the bits of code that tie a Firefox preference to an HKLM registry key, which can be read by the warm-up service at start-up. The next step is to add a mode to the Firefox executable that loads its core DLLs and then exits, and then have the warm-up service call into that mode if enabled.

Once this is done, we should be in a state where we can user test this feature.

Startup Cache Telemetry (In-Progress by Doug Thayer)

Two things of note here:

With the probes having now uplifted to Beta, data will slowly trickle in these next few days that will show us how the Firefox startup cache is behaving in the wild for users that aren’t receiving two updates a day (like our Nightly users). This important, because oftentimes, those updates cause some or all of the startup cache to be invalidated. We’re eager to see how the startup caches are behaving in the wild on Beta.

One of the tests that was landed for the startup cache Telemetry appears to have caught an issue with how the QuantumBar code works with it – this is useful, because up until now, we’ve had very little automated testing to ensure that the startup cache is working as expected.

Smoother Tab Animations (Paused by Felipe Gomes)

UX, Product and Engineering have been having discussions about how the new tab animations work, and one thing has been decided upon: we want our User Research team to run some studies to see how tab animations are perceived before we fully commit to changing one of the fundamental interactions in the browser. So, at this time, Felipe is pausing his efforts here until User Research comes back with some information on guidance.

Browser Adjustment Project (Concluded by Gijs Kruitbosch)

We originally set out to see whether or not we could do something for users running weaker hardware to improve their browsing experience. Our initial hypothesis was that by lowering the frame rate of the browser on weaker hardware, we could improve the overall page load time.

This hypothesis was bolstered by measurements done in late 2018, where it appeared that by manually lowering the frame rate on a weaker reference laptop, we could improve our internal page load benchmarks by a significant degree. This measurement was reproduced by Denis Palmeiro on Vicky Chin’s team, and so Gijs started implementing a runtime detection mechanism to do that lowering of the frame rate for machines with 2 or fewer cores where each core’s clockspeed was 1.8Ghz or slower1.

However, since then, we’ve been unable to reproduce the same positive effect on page load time. Neither has Denis. We suspect that recent work on the RefreshDriver, which changes how often the RefreshDriver runs during the page load window, is effectively getting the same kind of win2.

We did one final experiment to see whether or not lowering the frame rate would improve battery life, and it appeared to, but not to a very high degree. We might revisit that route were we tasked with trying to improve power usage in Firefox.

Experiments with the Process Priority Manager (In-Progress by Mike Conley)

I had a meeting today with Saptarshi, one of our illustrious Data Scientists, to talk about the upcoming experiment. One of the things he led me to conclude was that this experiment is going to have a lot of confounds, and it will be difficult to conclude things from.

Part of the reason for that is because there are often times when a background tab won’t actually have its content process priority lowered. The potential reasons for this are:

The tab is running in a content process which is also hosting a tab that is running in the foreground of either the same or some other browser window.

The tab is playing audio or video.

Because of this, we can’t actually do things like measure how page load is being impacted by this feature because we don’t have a great sense of how many tabs have their content process priorities lowered. That’s just not a thing we collect with Telemetry. It’s theoretically possible, either due to how many windows or videos or tabs our Beta users have open, that very few of them will ever actually have their content process priorities lowered, and then the data we’d draw from Telemetry would be useless.

I’m working with Saptarshi now to try to find ways of either altering the process priority manager or adding new probes to reduce the number of potential confounds.

dthayer is still trucking along here – he’s ironed out a number of glitches, and kats is giving feedback on some APZ-related changes. dthayer is also working on a WebRender API endpoint for generating frames for multiple documents in a single transaction, which should help reduce the window of opportunity for nasty synchronization bugs.

Warm-up Service (In-Progress by Doug Thayer)

dthayer is pressing ahead with this experiment to warm up a number of critical files for Firefox shortly after the OS boots. He is working on a prototype that can be controlled via a pref that we’ll be able to test on users in a lab-setting (and perhaps in the wild as a SHIELD experiment).

Startup Cache Telemetry (In-Progress by Doug Thayer)

dthayer landed this Telemetry early in the week, and data has started to trickle in. After a few more days, it should be easier for us to make inferences on how the startup caches are operating out in the wild for our Nightly users.

Smoother Tab Animations (In-Progress by Felipe Gomes)

Lazier Hidden Window (Completed by Felipe Gomes)

After a few rounds of landings and backouts, this appears to have stuck! The hidden window is now created after the main window has finished painting, and this has resulted in a nice ts_paint (startup paint) win on our Talos benchmark!

This is a graph of the ts_paint startup paint Talos benchmark. The highlighted node is the first mozilla-central build with the hidden window work. Lower is better, so this looks like a nice win!

Browser Adjustment Project (In-Progress by Gijs Kruitbosch)

This project appears to be reaching its conclusion, but with rather unsatisfying results. Denis Palmeiro from Vicky Chin’s team has done a bunch of testing of both the original set of patches that Gijs landed to lower the global frame rate (painting and compositing) from 60fps to 30fps for low-end machines, as well as the new patches that decrease the frequency of main-thread painting (but not compositing) to 30fps. Unfortunately, this has not yielded the page load wins that we wanted1. We’re still waiting to see if there’s a least a power-usage win here worth pursuing, but we’re almost ready the pull the plug on this one.

Experiments with the Process Priority Manager (In-Progress by Mike Conley)

The Process Priority Manager has been enabled in Nightly for a number of weeks now, and no new bugs have been filed against it. I filed a bug earlier this week to run a pref-flip experiment on Beta after the Process Priority Manager patches are uplifted later this month. Our hope is that this has a neutral or positive impact on both page load time and user retention!

Make the PageStyleChild load lazily (Completed by Mike Conley)

There’s an infrequently used feature in Firefox that allows users to switch between different CSS stylesheets that a page might offer. I’ve made the component that scans the document for alternative stylesheets much lazier, and also made it skip non web-pages, which means (at the very least) less code running when loading about:home and about:newtab

This was unexpected – we ran an experiment late in 2018 where we noticed that lowering the frame rate manually via the layout.frame_rate pref had a positive impact on page load time… unfortunately, this effect is no longer being observed. This might be due to other refresh driver work that has occurred in the meantime. ↩